[Minimal-Intervention on smoking cessation: a Meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate the effectiveness of Minimal Smoking Cessation Intervention Program (MSCIP) and to provide theoretical basis for the feasibility of implementation in China. METHODS: Systematically, we searched data from studies published between January, 2000 and September, 2014 on the database that including Cochrane Library, Medline, EMbase, CNKI, Wanfang, Vip, etc. Studies related to MSCIP were designed by random controlled trials. Meta analysis was performed by Revman 5.1. RESULTS: Nine studies were included, with the Random-Effect Model Relative Risk as 1.57 (1.01-2.44), which indicated that the probability of being tobacco abstinent had increased by 57% in the treating group than in the control group. Participants who developed other diseases, being pregnant or the time of receiving intervention messages ≤ 10 minutes, were more likely to quit the program. There were no significant statistically differences noticed between the different subgroups. CONCLUSION: Minimal smoking cessation intervention increased cessation rates, RCTs with a larger sample size are needed to draw the related conclusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".